Package weka.classifiers.meta
Class CVParameterSelection
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.SingleClassifierEnhancer
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- weka.classifiers.RandomizableSingleClassifierEnhancer
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- weka.classifiers.meta.CVParameterSelection
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,CapabilitiesHandler
,Drawable
,OptionHandler
,Randomizable
,RevisionHandler
,Summarizable
,TechnicalInformationHandler
public class CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Drawable, Summarizable, TechnicalInformationHandler
Class for performing parameter selection by cross-validation for any classifier.
For more information, see:
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University. BibTeX:@phdthesis{Kohavi1995, address = {Department of Computer Science, Stanford University}, author = {R. Kohavi}, school = {Stanford University}, title = {Wrappers for Performance Enhancement and Oblivious Decision Graphs}, year = {1995} }
Valid options are:-X <number of folds> Number of folds used for cross validation (default 10).
-P <classifier parameter> Classifier parameter options. eg: "N 1 5 10" Sets an optimisation parameter for the classifier with name -N, with lower bound 1, upper bound 5, and 10 optimisation steps. The upper bound may be the character 'A' or 'I' to substitute the number of attributes or instances in the training data, respectively. This parameter may be supplied more than once to optimise over several classifier options simultaneously.
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
Options after -- are passed to the designated sub-classifier.- Version:
- $Revision: 8180 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Field Summary
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Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE
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Constructor Summary
Constructors Constructor Description CVParameterSelection()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
addCVParameter(java.lang.String cvParam)
Adds a scheme parameter to the list of parameters to be set by cross-validationvoid
buildClassifier(Instances instances)
Generates the classifier.java.lang.String
CVParametersTipText()
Returns the tip text for this propertydouble[]
distributionForInstance(Instance instance)
Predicts the class distribution for the given test instance.java.lang.String[]
getBestClassifierOptions()
Returns (a copy of) the best options found for the classifier.Capabilities
getCapabilities()
Returns default capabilities of the classifier.java.lang.String
getCVParameter(int index)
Gets the scheme paramter with the given index.java.lang.Object[]
getCVParameters()
Get method for CVParameters.int
getNumFolds()
Gets the number of folds for the cross-validation.java.lang.String[]
getOptions()
Gets the current settings of the Classifier.java.lang.String
getRevision()
Returns the revision string.TechnicalInformation
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.java.lang.String
globalInfo()
Returns a string describing this classifierjava.lang.String
graph()
Returns graph describing the classifier (if possible).int
graphType()
Returns the type of graph this classifier represents.java.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
numFoldsTipText()
Returns the tip text for this propertyvoid
setCVParameters(java.lang.Object[] params)
Set method for CVParameters.void
setNumFolds(int numFolds)
Sets the number of folds for the cross-validation.void
setOptions(java.lang.String[] options)
Parses a given list of options.java.lang.String
toString()
Returns description of the cross-validated classifier.java.lang.String
toSummaryString()
A concise description of the model.-
Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
getSeed, seedTipText, setSeed
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Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing this classifier- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-X <number of folds> Number of folds used for cross validation (default 10).
-P <classifier parameter> Classifier parameter options. eg: "N 1 5 10" Sets an optimisation parameter for the classifier with name -N, with lower bound 1, upper bound 5, and 10 optimisation steps. The upper bound may be the character 'A' or 'I' to substitute the number of attributes or instances in the training data, respectively. This parameter may be supplied more than once to optimise over several classifier options simultaneously.
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
Options after -- are passed to the designated sub-classifier.- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classRandomizableSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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getBestClassifierOptions
public java.lang.String[] getBestClassifierOptions()
Returns (a copy of) the best options found for the classifier.- Returns:
- the best options
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Generates the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
instances
- set of instances serving as training data- Throws:
java.lang.Exception
- if the classifier has not been generated successfully
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Predicts the class distribution for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- the predicted class value
- Throws:
java.lang.Exception
- if an error occurred during the prediction
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addCVParameter
public void addCVParameter(java.lang.String cvParam) throws java.lang.Exception
Adds a scheme parameter to the list of parameters to be set by cross-validation- Parameters:
cvParam
- the string representation of a scheme parameter. The format is:
param_char lower_bound upper_bound number_of_steps
eg to search a parameter -P from 1 to 10 by increments of 1:
P 1 10 11- Throws:
java.lang.Exception
- if the parameter specifier is of the wrong format
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getCVParameter
public java.lang.String getCVParameter(int index)
Gets the scheme paramter with the given index.- Parameters:
index
- the index for the parameter- Returns:
- the scheme parameter
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CVParametersTipText
public java.lang.String CVParametersTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getCVParameters
public java.lang.Object[] getCVParameters()
Get method for CVParameters.- Returns:
- the CVParameters
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setCVParameters
public void setCVParameters(java.lang.Object[] params) throws java.lang.Exception
Set method for CVParameters.- Parameters:
params
- the CVParameters to use- Throws:
java.lang.Exception
- if the setting of the CVParameters fails
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numFoldsTipText
public java.lang.String numFoldsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getNumFolds
public int getNumFolds()
Gets the number of folds for the cross-validation.- Returns:
- the number of folds for the cross-validation
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setNumFolds
public void setNumFolds(int numFolds) throws java.lang.Exception
Sets the number of folds for the cross-validation.- Parameters:
numFolds
- the number of folds for the cross-validation- Throws:
java.lang.Exception
- if parameter illegal
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graphType
public int graphType()
Returns the type of graph this classifier represents.
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graph
public java.lang.String graph() throws java.lang.Exception
Returns graph describing the classifier (if possible).
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toString
public java.lang.String toString()
Returns description of the cross-validated classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- description of the cross-validated classifier as a string
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toSummaryString
public java.lang.String toSummaryString()
A concise description of the model.- Specified by:
toSummaryString
in interfaceSummarizable
- Returns:
- a concise description of the model
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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